Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Andres D. Perez"'
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 83, Iss 12, Pp 1-14 (2023)
Abstract Machine-learned likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensional data sets. We extend the MLL method by including kernel
Externí odkaz:
https://doaj.org/article/27abc3497fb14803b94a5c3ee3930c74
Autor:
Ernesto Arganda, Xabier Marcano, Víctor Martín Lozano, Anibal D. Medina, Andres D. Perez, Manuel Szewc, Alejandro Szynkman
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 82, Iss 11, Pp 1-14 (2022)
Abstract Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the signal-
Externí odkaz:
https://doaj.org/article/0adb5e6ff5c84c89a51425a2b66fb16c
Publikováno v:
Nuclear Physics B, Vol 974, Iss , Pp 115637- (2022)
In this paper we investigate the (g−2)μ discrepancy in the context of the R-parity conserving next-to-minimal supersymmetric Standard Model plus right-handed neutrinos superfields. The model has the ability to reproduce neutrino physics data and i
Externí odkaz:
https://doaj.org/article/51dc60a6536943959caf6acd2b4d3c23
Publikováno v:
Nuclear Physics B, Vol 974, Iss, Pp 115637-(2022)
Nuclear Physics
Nuclear Physics
In this paper we investigate the $(g-2)_\mu$ discrepancy in the context of the R-parity conserving next-to-minimal supersymmetric Standard Model plus right-handed neutrinos superfields. The model has the ability to reproduce neutrino physics data and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97f55fd2c8e245a324af3e14f7bab726
http://arxiv.org/abs/2107.02285
http://arxiv.org/abs/2107.02285
R-parity conserving supersymmetric models with right-handed (RH) neutrinos are very appealing since they could naturally explain neutrino physics and also provide a good dark matter (DM) candidate such as the lightest supersymmetric particle (LSP). I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2df7cd910a49af5d96bd666c04e2ee6f
http://arxiv.org/abs/2102.08986
http://arxiv.org/abs/2102.08986
Publikováno v:
Astroparticle Physics. 125:102506
In R-parity violating supersymmetry, the gravitino as the lightest supersymmetric particle (LSP) is a good candidate for dark matter, with the interesting characteristic to be detectable through γ-ray telescopes. We extend this analysis considering
Publikováno v:
Journal of Cosmology and Astroparticle Physics. 2020:058-058
Axino and gravitino are promising candidates to solve the dark matter (DM) problem in the framework of supersymmetry. In this work, we assume that the axino is the lightest supersymmetric particle (LSP), and therefore contributes to DM. In the case o
Autor:
G. A. Gomez-Vargas, Daniel E. López-Fogliani, Roberto Ruiz de Austri, Carlos Muñoz, Andres D. Perez
Publikováno v:
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
The $\mu\nu$SSM solves the $\mu$ problem of supersymmetric models and reproduces neutrino data, simply using couplings with right-handed neutrinos $\nu$'s. Given that these couplings break explicitly $R$ parity, the gravitino is a natural candidate f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dad2fee0609e07b8a6bda8b069eed191
Autor:
Victor Arevalo Cabra, Karen Chávez Quintero, Andrés D. Pérez, Grace Torres Pineda, Julieth Solano Villa, Vahan Martirosyan
Publikováno v:
Citizen Science: Theory and Practice, Vol 8, Iss 1, Pp 38-38 (2023)
In recent years, the use of nontraditional data sources in statistical production has been increasing, given the additional need for more timely and disaggregated data. In the scope of nontraditional sources, citizen science represents an innovative
Externí odkaz:
https://doaj.org/article/77c64634110d400689b6f52dadce8c58
Publikováno v:
SciPost Physics, Vol 12, Iss 2, p 063 (2022)
We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms inst
Externí odkaz:
https://doaj.org/article/db19688a8eb148fc8df2ab89360c3d39